| Season | Home Runs |
|---|---|
| 2015 | 4909 |
| 2016 | 5610 |
| 2017 | 6105 |
| 2018 | 5585 |
| 2019 | 6776 |
| 2021 | 5944 |
| 2022 | 5215 |
| 2023 | 5868 |
2024-02-01
3rd edition (with Max Marchi and Ben Baumer) available soon
Online version available at https://beanumber.github.io/abdwr3e/
Book blog at https://baseballwithr.wordpress.com/
60 posts on aspects of home run hitting
Baseball Reference provides the average number of Home Runs, Hits, Runs for each team each game during all seasons of Major League Baseball (1871 through 2023)
Home Run hitting has gone through many changes in MLB History
| Season | Home Runs |
|---|---|
| 2015 | 4909 |
| 2016 | 5610 |
| 2017 | 6105 |
| 2018 | 5585 |
| 2019 | 6776 |
| 2021 | 5944 |
| 2022 | 5215 |
| 2023 | 5868 |
Three Basic Outcomes:
How have the rates of these three outcomes changed over the last 50 years of baseball?
SO Rates have been steadily increasing
In-Play Rates have been decreasing
Pattern of Walk Rates are less clear, show up and down movement
Define the home run rate as the fraction of \(HR\) among all batted balls (\(AB - SO\))\[ HR \, Rate = \frac{HR}{AB - SO} \]
Look at history of \(HR\) rates
Fall of 2017 a committee was charged by Major League Baseball to identify the potential causes of the increase in the rate at which home runs were hit in 2015, 2016, and 2017.
Report was released in May 2018.
The batters?
The pitchers?
The ball?
Game conditions?
IN-PLAY: Have to put the ball in play
HIT IT RIGHT: The batted ball needs to have the “right” launch angle and exit velocity
REACH THE SEATS: Given the exit velocity and launch angle, needs to have sufficient distance and height to clear the fence (the carry of ball)
Hit 62 home runs during a season when the ball was relatively dead
Raises the question: How many home runs would Judge hit during a different season during Statcast era?
Suppose the different season is 2019.
Fit a “2019 ball model” that predicts the probability of a HR in 2019 given values of the launch angle and exit velocity.
Collect the launch variables for Judge for all balls put into play. For each BIP, predict P(HR) using 2019 ball model.
Sum the probabilities – predict the season HR.
\(logit(P(HR)) = s(LA, LS)\)
\(s()\) is a smooth function of the launch angle (LA) and the launch speed (LS)
Generalization of the linear regression model \(y = X \beta + \epsilon\)
For each Judge’s BIP in 2022, predict the probability of HR from the launch variables using the 2019 ball model.
Sum the probabilities – predict total HR count
Can get a 90% prediction interval
If Judge was hitting using a 2019 ball, predict he would hit 75 home runs
A 90% prediction interval would be (69, 81)
Use GAM model to predict prob(HR) from the launch angle and exit velocity for one season
Use this ball model to predict HR probability using 2022 launch variables
Sum prediction probabilities
Judge only hit 62 home runs in 2022
But if he was playing during a different Statcast season where the ball was more alive (more carry), we’d predict his 2022 count to be in the 70’s
So Judge’s home run achievement is understated
Due to this ball bias, we don’t appreciate magnitude of Judge’s accomplishment
Many factors influence home run hitting.
Two important factors are the hitters (values of launch variables) and the ball (carry or drag coefficient).
It is helpful to monitor the Balls-in-Play and Home Run rates to see the effects due to the hitters and the ball.
Batters are stronger and changing their hitting approach, leading to higher rates of “HR friendly” balls in play.
The composition of the ball has gone through dramatic changes during the Statcast era.
Currently the ball is relatively dead compared to previous seasons.